The priority species indicator: measuring the trends in threatened species in the UK

A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data

Biodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of time-varying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a state-space formulation for new multispecies biodiversity indicators, based on rates of change in the abundance or occupancy probability of the contributing individual species. The formulation is flexible and applicable to different taxa. It possesses several advantages, including the ability to accommodate the sporadic unavailability of data, incorporate variation in the estimation precision of the individual species’ indices when appropriate, and allow the direct incorporation of smoothing over time. Furthermore, model fitting is straightforward in Bayesian and classical implementations, the latter adopting either efficient Hidden Markov modelling or the Kalman filter. Conveniently, the same algorithms can be adopted for cases based on abundance or occupancy data—only the subsequent interpretation differs. The procedure removes the need for bootstrapping which can be prohibitive. We recommend which of two alternatives to use when taxa are fully or partially sampled. The performance of the new approach is demonstrated on simulated data, and through application to three diverse national UK data sets on butterflies, bats and dragonflies. We see that uncritical incorporation of index standard errors should be avoided.

Supplementary materials accompanying this paper appear online.

Annual estimates of occupancy for bryophytes, lichens and invertebrates in the UK, 1970-2015

Here, we determine annual estimates of occupancy and species trends for 5,293 UK bryophytes, lichens, and invertebrates, providing national scale information on UK biodiversity change for 31 taxonomic groups for the time period 1970 to 2015. The dataset was produced through the application of a Bayesian occupancy modelling framework to species occurrence records supplied by 29 national recording schemes or societies (n = 24,118,549 records). In the UK, annual measures of species status from fine scale data (e.g. 1 × 1 km) had previously been limited to a few taxa for which structured monitoring data are available, mainly birds, butterflies, bats and a subset of moth species. By using an occupancy modelling framework designed for use with relatively low recording intensity data, we have been able to estimate species trends and generate annual estimates of occupancy for taxa where annual trend estimates and status were previously limited or unknown at this scale. These data broaden our knowledge of UK biodiversity and can be used to investigate variation in and drivers of biodiversity change.

Using citizen science butterfly counts to predict species population trends

Citizen scientists are increasingly engaged in gathering biodiversity information, but trade-offs are often required between public engagement goals and reliable data collection. We compared population estimates for 18 widespread butterfly species derived from the first 4 years (2011-2014) of a short-duration citizen science project (Big Butterfly Count [BBC]) with those from long-running, standardized monitoring data collected by experienced observers (U.K. Butterfly Monitoring Scheme [UKBMS]). BBC data are gathered during an annual 3-week period, whereas UKBMS sampling takes place over 6 months each year. An initial comparison with UKBMS data restricted to the 3-week BBC period revealed that species population changes were significantly correlated between the 2 sources. The short-duration sampling season rendered BBC counts susceptible to bias caused by interannual phenological variation in the timing of species' flight periods. The BBC counts were positively related to butterfly phenology and sampling effort. Annual estimates of species abundance and population trends predicted from models including BBC data and weather covariates as a proxy for phenology correlated significantly with those derived from UKBMS data. Overall, citizen science data obtained using a simple sampling protocol produced comparable estimates of butterfly species abundance to data collected through standardized monitoring methods. Although caution is urged in extrapolating from this U.K. study of a small number of common, conspicuous insects, we found that mass-participation citizen science can simultaneously contribute to public engagement and biodiversity monitoring. Mass-participation citizen science is not an adequate replacement for standardized biodiversity monitoring but may extend and complement it (e.g., through sampling different land-use types), as well as serving to reconnect an increasingly urban human population with nature.